This package implements model-robust standardization for cluster-randomized trials (CRTs). Provides functions that standardize user-specified regression models to estimate marginal treatment effects. The targets include the cluster-average and individual-average treatment effects, with utilities for variance estimation and example simulation datasets. Methods are described in Li, Tong, Fang, Cheng, Kahan, and Wang (2025) <doi:10.1002/sim.70270>.

Allows practitioners and researchers a wholesale approach for deriving magnitude-based inferences from raw data. A major goal of mbir is to programmatically detect appropriate statistical tests to run in lieu of relying on practitioners to determine correct stepwise procedures independently.

The effects of the site may severely bias the accuracy of a multisite machine-learning model, even if the analysts removed them when fitting the model in the training set and applying the model in the test set (Solanes et al., Neuroimage 2023, 265:119800). This simple R package estimates the accuracy of a multisite machine-learning model unbiasedly, as described in (Solanes et al., Psychiatry Research: Neuroimaging 2021, 314:111313). It currently supports the estimation of sensitivity, specificity, balanced accuracy (for binary or multinomial variables), the area under the curve, correlation, mean squarer error, and hazard ratio for binomial, multinomial, gaussian, and survival (time-to-event) outcomes.

This package performs multiple imputation of missing data using an ensemble super learner built with the tidymodels framework. For each incomplete column, a stacked ensemble of candidate learners is trained on a bootstrap sample of the observed data and used to generate imputations via predictive mean matching (continuous), probability draws (binary), or cumulative probability draws (categorical). Supports parallelism across imputed datasets via the future framework.

This package provides a series of functions to implement association of covariance for detecting differential co-expression (ACDC), a novel approach for detection of differential co-expression that simultaneously accommodates multiple phenotypes or exposures with binary, ordinal, or continuous data types. Users can use the default method which identifies modules by Partition or may supply their own modules. Also included are functions to choose an information loss criterion (ILC) for Partition using OmicS-data-based Complex trait Analysis (OSCA) and Genome-wide Complex trait Analysis (GCTA). The manuscript describing these methods is as follows: Queen K, Nguyen MN, Gilliland F, Chun S, Raby BA, Millstein J. "ACDC: a general approach for detecting phenotype or exposure associated co-expression" (2023) <doi:10.3389/fmed.2023.1118824>.

This package provides tools to conduct Bayesian multivariate network meta-analysis providing - the single correlation coefficient model by Efthimiou et al. (2015) <doi:10.1093/biostatistics/kxu030>; - per-outcome treatment hierarchies using the surface under the cumulative ranking curve (SUCRA), the probability of best value, or median (or mean) ranks (Salanti et al., 2011) <doi:10.1016/j.jclinepi.2010.03.016>; - across-outcomes benefit-risk assessment using the ViÅ¡eKriterijumska Optimizacija I Kompromisno ReÅ¡enje (VIKOR) method (Opricovic & Tzeng, 2004) <doi:10.1016/S0377-2217(03)00020-1>; - convergence checks using trace plots, density plots, or the R-hat statistic; - forest plots of treatment estimates, scatter plots of per-outcome rankings, Hasse diagrams (Carlsen & Bruggemann, 2014) <doi:10.1002/cem.2569> to visualize the partial order of the treatments across all outcomes.

Mica is a server application used to create data web portals for large-scale epidemiological studies or multiple-study consortia. Mica helps studies to provide scientifically robust data visibility and web presence without significant information technology effort. Mica provides a structured description of consortia, studies, annotated and searchable data dictionaries, and data access request management. This Mica client allows to perform data extraction for reporting purposes.

This package provides a collection of functions to download and process weather data from the Oklahoma Mesonet <https://mesonet.org>. Functions are available for downloading station metadata, downloading Mesonet time series (MTS) files, importing MTS files into R, and converting soil temperature change measurements into soil matric potential and volumetric soil moisture.

Estimate Multidimensional Poverty Index (MPI) measures from household survey microdata using the Alkire-Foster dual-cutoff counting method (Alkire and Foster, 2011). Load indicator specifications from CSV, Excel, JSON, or plain-text files; compute the headcount ratio (H), intensity (A), and MPI = H x A across any subgroup; and export results to formatted Excel reports. Supports complex survey designs â stratification, clustering, and probability weights â and optionally appends design-based standard errors and confidence intervals.

Automatic marking of R assignments for students and teachers based on testthat test suites.

Computing metabolite set enrichment analysis (MSEA) (Yamamoto, H. et al. (2014) <doi:10.1186/1471-2105-15-51>), single sample enrichment analysis (SSEA) (Yamamoto, H. (2023) <doi:10.51094/jxiv.262>) and over-representation analysis (ORA) that accounts for undetected metabolites (Yamamoto, H. (2024) <doi:10.51094/jxiv.954>).

This package provides tools to help visualize Major League Baseball analysis in ggplot2 and gt'. You provide team/player information and mlbplotR will transform that information into team colors, logos, or player headshots for graphics.

Exploratory data analysis and manipulation functions for multi- label data sets along with an interactive Shiny application to ease their use.

Enables us to use the functions of the package magick interactively.

This package provides tools for motif analysis in multi-level networks. Multi-level networks combine multiple networks in one, e.g. social-ecological networks. Motifs are small configurations of nodes and edges (subgraphs) occurring in networks. motifr can visualize multi-level networks, count multi-level network motifs and compare motif occurrences to baseline models. It also identifies contributions of existing or potential edges to motifs to find critical or missing edges. The package is in many parts an R wrapper for the excellent SESMotifAnalyser Python package written by Tim Seppelt.

Do multilevel mediation analysis with generalized additive multilevel models. The analysis method is described in Yu and Li (2020), "Third-Variable Effect Analysis with Multilevel Additive Models", PLoS ONE 15(10): e0241072.

Estimates probit, logit, Poisson, negative binomial, and beta regression models, returning their marginal effects, odds ratios, or incidence rate ratios as an output. Greene (2008, pp. 780-7) provides a textbook introduction to this topic.

This package implements methods for comparing sensitivities and specificities in balanced (or fully crossed) multi-reader multi-case (MRMC) studies with binary diagnostic test results. It implements conditional logistic regression and provides score tests equivalent to Cochran's Q test (which corresponds to McNemar's test when comparing two modalities only). The methodology is based on Lee et al. (2026) <doi:10.1002/sim.70471>.

Implementation of Multiple Comparison Procedures with Modeling (MCP-Mod) procedure with bias-corrected estimators and second-order covariance matrices as described in Diniz, Gallardo and Magalhaes (2023) <doi:10.1002/pst.2303>.

This package provides methods for detecting multivariate outliers in numeric datasets. The package implements classical Mahalanobis distance, robust Minimum Covariance Determinant (MCD), and Principal Component Analysis (PCA)-based approaches for outlier detection. The methodology is informed by Aggarwal (2017) <doi:10.1007/978-3-319-47578-3> and Grentzelos, Caroni and Barranco-Chamorro (2020) <doi:10.1002/cmm4.1129>. Visualization functions are included to aid interpretation of detected outliers. Mahalanobis distance calculations are accelerated using C++ through Rcpp'.

Various reliability analysis methods for rare event inference (computing failure probability and quantile from model/function outputs).

Data and examples from a multilevel modelling software review as well as other well-known data sets from the multilevel modelling literature.

The base apply function and its variants, as well as the related functions in the plyr package, typically apply user-defined functions to a single argument (or a list of vectorized arguments in the case of mapply). The multiApply package extends this paradigm with its only function, Apply, which efficiently applies functions taking one or a list of multiple unidimensional or multidimensional arrays (or combinations thereof) as input. The input arrays can have different numbers of dimensions as well as different dimension lengths, and the applied function can return one or a list of unidimensional or multidimensional arrays as output. This saves development time by preventing the R user from writing often error-prone and memory-inefficient loops dealing with multiple complex arrays. Also, a remarkable feature of Apply is the transparent use of multi-core through its parameter ncores'. In contrast to the base apply function, this package suggests the use of target dimensions as opposite to the margins for specifying the dimensions relevant to the function to be applied.

This package provides tools for analysing multivariate time series with wavelets. This includes: simulation of a multivariate locally stationary wavelet (mvLSW) process from a multivariate evolutionary wavelet spectrum (mvEWS); estimation of the mvEWS, local coherence and local partial coherence. See Park, Eckley and Ombao (2014) <doi:10.1109/TSP.2014.2343937> for details.